Language/12_01_TRL_SFT.ipynb

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        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/peisuke/generative_ai_notebooks/blob/main/12_01_TRL_SFT.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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      "metadata": {
        "colab": {
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        "outputId": "8a18a118-22e6-40b6-8c00-ee466f9e0417"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "# Googleドライブのマウント\n",
        "from google.colab import drive\n",
        "drive.mount(\"/content/drive\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# パッケージのインストール\n",
        "!pip install -q accelerate==0.21.0 peft==0.4.0 bitsandbytes==0.40.2 transformers==4.31.0 trl==0.4.7 sentencepiece"
      ],
      "metadata": {
        "colab": {
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m244.2/244.2 kB\u001b[0m \u001b[31m4.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m72.9/72.9 kB\u001b[0m \u001b[31m6.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m92.5/92.5 MB\u001b[0m \u001b[31m18.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.4/7.4 MB\u001b[0m \u001b[31m98.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m77.4/77.4 kB\u001b[0m \u001b[31m9.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m71.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m88.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m521.2/521.2 kB\u001b[0m \u001b[31m28.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.3/115.3 kB\u001b[0m \u001b[31m15.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m17.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
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    },
    {
      "cell_type": "code",
      "source": [
        "# HuggingFaceのログイン\n",
        "!huggingface-cli login"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DkOihlbkUkyv",
        "outputId": "6d2a08af-b34a-411a-e883-d1e5d0981056"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "    _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
            "    _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
            "    _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n",
            "    _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n",
            "    _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n",
            "\n",
            "    To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n",
            "Token: \n",
            "Add token as git credential? (Y/n) n\n",
            "Token is valid (permission: read).\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# パッケージのインポート\n",
        "import os\n",
        "import torch\n",
        "from datasets import load_dataset\n",
        "from transformers import (\n",
        "    AutoModelForCausalLM,\n",
        "    AutoTokenizer,\n",
        "    BitsAndBytesConfig,\n",
        "    HfArgumentParser,\n",
        "    TrainingArguments,\n",
        "    pipeline,\n",
        "    logging,\n",
        ")\n",
        "from peft import LoraConfig, PeftModel\n",
        "from trl import SFTTrainer"
      ],
      "metadata": {
        "id": "-Yh4jlm0Uouj"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# データセットの読み込み読み込み\n",
        "dataset = load_dataset(\"kunishou/databricks-dolly-15k-ja\", split=\"train\")"
      ],
      "metadata": {
        "id": "2suOM7lcU14P"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# プロンプトテンプレートの準備\n",
        "def generate_prompt(data_point):\n",
        "    if data_point[\"input\"]:\n",
        "        result = f\"\"\"[INST] {data_point[\"instruction\"]}\\n\\n{data_point[\"input\"]} [/INST] {data_point[\"output\"]}\"\"\"\n",
        "    else:\n",
        "        result = f\"\"\"[INST] {data_point[\"instruction\"]} [/INST] {data_point[\"output\"]}\"\"\"\n",
        "    return result\n",
        "\n",
        "# テキスト列の追加\n",
        "def add_text(example):\n",
        "    example[\"text\"] = generate_prompt(example)\n",
        "    del example[\"index\"]\n",
        "    del example[\"category\"]\n",
        "    del example[\"instruction\"]\n",
        "    del example[\"input\"]\n",
        "    del example[\"output\"]\n",
        "    return example\n",
        "\n",
        "dataset = dataset.map(add_text)"
      ],
      "metadata": {
        "id": "LtkunHhVVFc3"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(dataset)\n",
        "print(dataset[0][\"text\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sjO4f73MYBpI",
        "outputId": "864bcf8d-3b70-4c2b-f5b9-314f997d6cb3"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset({\n",
            "    features: ['text'],\n",
            "    num_rows: 15015\n",
            "})\n",
            "[INST] ヴァージン・オーストラリア航空はいつから運航を開始したのですか？\n",
            "\n",
            "ヴァージン・オーストラリア航空（Virgin Australia Airlines Pty Ltd）はオーストラリアを拠点とするヴァージン・ブランドを冠する最大の船団規模を持つ航空会社です。2000年8月31日に、ヴァージン・ブルー空港として、2機の航空機、1つの空路を運行してサービスを開始しました。2001年9月のアンセット・オーストラリア空港の崩壊後、オーストラリアの国内市場で急速に地位を確立しました。その後はブリスベン、メルボルン、シドニーをハブとして、オーストラリア国内の32都市に直接乗り入れるまでに成長しました。 [/INST] ヴァージン・オーストラリア航空は、2000年8月31日にヴァージン・ブルー航空として、2機の航空機で単一路線の運航を開始しました。\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 量子化パラメータ\n",
        "bnb_config = BitsAndBytesConfig(\n",
        "    load_in_4bit=True,\n",
        "    bnb_4bit_quant_type=\"nf4\",\n",
        "    bnb_4bit_compute_dtype=torch.float16,\n",
        "    bnb_4bit_use_double_quant=False,\n",
        ")\n",
        "\n",
        "# モデルの準備\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    \"meta-llama/Llama-2-7b-chat-hf\",\n",
        "    quantization_config=bnb_config,\n",
        "    device_map={\"\": 0}\n",
        ")\n",
        "model.config.use_cache = False\n",
        "model.config.pretraining_tp = 1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241,
          "referenced_widgets": [
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        "outputId": "425b5ceb-7f49-4299-ebaa-848a9e19552a"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
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            "text/plain": [
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        {
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        {
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          "data": {
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        {
          "output_type": "display_data",
          "data": {
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        {
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          "data": {
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
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        {
          "output_type": "display_data",
          "data": {
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    },
    {
      "cell_type": "code",
      "source": [
        "# トークナイザーの準備\n",
        "tokenizer = AutoTokenizer.from_pretrained(\n",
        "    \"meta-llama/Llama-2-7b-chat-hf\",\n",
        "    use_fast=False,\n",
        "    add_eos_token=True,  # データへのEOSの追加を指示\n",
        "    trust_remote_code=True\n",
        ")\n",
        "tokenizer.pad_token = tokenizer.unk_token\n",
        "tokenizer.padding_side = \"right\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
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        },
        "id": "R7EdBd0vWWR_",
        "outputId": "fbc9c0d3-4fab-4be4-b5b0-2c53ffa7ce45"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/1.62k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "10586f90310f4314838c41c324146b46"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.model:   0%|          | 0.00/500k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4bf68fc778cc421a815d12eb1a321dc7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/414 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c55d5a7443724669beab2c67ef6c87ad"
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# LoRAパラメータ\n",
        "peft_config = LoraConfig(\n",
        "    r=64,\n",
        "    lora_alpha=16,\n",
        "    lora_dropout=0.1,\n",
        "    bias=\"none\",\n",
        "    task_type=\"CAUSAL_LM\",\n",
        "    target_modules=[\"q_proj\", \"o_proj\", \"gate_proj\", \"up_proj\", \"down_proj\", \"k_proj\", \"v_proj\"]\n",
        ")\n",
        "\n",
        "# 学習パラメータ\n",
        "training_arguments = TrainingArguments(\n",
        "    output_dir=\"./train_logs\",\n",
        "    fp16=True,\n",
        "    bf16=False,\n",
        "    max_steps=300,\n",
        "    per_device_train_batch_size=4,\n",
        "    gradient_accumulation_steps=1,\n",
        "    optim=\"paged_adamw_32bit\",\n",
        "    learning_rate=2e-4,\n",
        "    lr_scheduler_type=\"cosine\",\n",
        "    max_grad_norm=0.3,\n",
        "    warmup_ratio=0.03,\n",
        "    weight_decay=0.001,\n",
        "    save_steps=100,\n",
        "    logging_steps=25,\n",
        "    group_by_length=True,\n",
        "    report_to=\"tensorboard\"\n",
        ")\n",
        "\n",
        "# SFTパラメータ\n",
        "trainer = SFTTrainer(\n",
        "    model=model,\n",
        "    tokenizer=tokenizer,\n",
        "    train_dataset=dataset,\n",
        "    dataset_text_field=\"text\",  # データセットのtext列\n",
        "    peft_config=peft_config,\n",
        "    args=training_arguments,\n",
        "    max_seq_length=None,\n",
        "    packing=False,\n",
        ")\n",
        "\n",
        "# モデルの学習\n",
        "trainer.train()\n",
        "trainer.model.save_pretrained(\"/content/drive/MyDrive/AI/RLHF/lora_model\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 687,
          "referenced_widgets": [
            "8c81c6739a2e4e8a925264aec3a270d2",
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            "e15bd237d0484da9b30da8e32ef7ca90"
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        },
        "id": "TpLhNdpXWkF1",
        "outputId": "3ae8e545-6a8e-4eb4-faf0-fa0ba9ad67d4"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/peft/utils/other.py:102: FutureWarning: prepare_model_for_int8_training is deprecated and will be removed in a future version. Use prepare_model_for_kbit_training instead.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py:159: UserWarning: You didn't pass a `max_seq_length` argument to the SFTTrainer, this will default to 1024\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/15015 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8c81c6739a2e4e8a925264aec3a270d2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='300' max='300' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [300/300 29:14, Epoch 0/1]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>25</td>\n",
              "      <td>1.488300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>1.384200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>75</td>\n",
              "      <td>1.275600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>1.127400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>125</td>\n",
              "      <td>1.216300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>150</td>\n",
              "      <td>1.083100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>175</td>\n",
              "      <td>1.202300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>1.378200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>225</td>\n",
              "      <td>1.375900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>250</td>\n",
              "      <td>1.699700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>275</td>\n",
              "      <td>1.339600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>300</td>\n",
              "      <td>1.711700</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# プロンプトの準備\n",
        "prompt = \"[INST] 富士山の高さは？ [/INST] \"\n",
        "\n",
        "# 推論の実行\n",
        "input_ids = tokenizer(prompt, add_special_tokens=False, return_tensors='pt')\n",
        "output_ids = model.generate(\n",
        "    **input_ids.to(model.device),\n",
        "    max_new_tokens=100,\n",
        "    do_sample=True,\n",
        "    temperature=0.3,\n",
        ")\n",
        "output = tokenizer.decode(output_ids.tolist()[0])\n",
        "print(output)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XlT5U2rvXNit",
        "outputId": "327e8f5b-a828-4f13-d101-893199affc98"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:61: UserWarning: None of the inputs have requires_grad=True. Gradients will be None\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " [INST] 富士山の高さは？ [/INST] 富士山の高さは、3776メートルです。</s>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "sBP8ofyeeVqC"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}